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NNs are not binary -- they're fundamentally analog, so for classification problems, they produce probabilities that a test case is in each possible class. A bin
by mgurlitz 8y ago
NNs are not binary -- they're fundamentally analog, so for classification problems, they produce probabilities that a test case is in each possible class. A binary "X/not-X" test often comes from applying a threshold to the NN's output.
Quoting from the Universal approximation theory's Wikipedia, "neural networks can represent a wide variety of interesting functions." While they may be much better at pattern recognition, it's possible to produce almost anything with one, including Go moves, if you can devise a method to interpret the outputs.
- iandanforth 8y ago>> NNs are not binary -- they're fundamentally analog That's incorrect. https://arxiv.org/abs/1602.02830 https://arxiv.org/abs/1602.02830
- candiodari 8y agoYou could say they're "probabilistically binary", that would at least be intuitively accurate.
- joe_the_user 8y agoNNs are not binary -- they're fundamentally analog, so for classification problems, they produce probabilities that a test case is in each possible class. I think that's "true and false". Yes, NNs produce a "chance of being X" but no, NNs don't naturally produce a human-intuitive "degree of being X". If the NN is looking for a "red fire engine", it's not necessarily going to produce "degree of redness" as its output. -- As to neural nets representing many functions; neural nets, Taylor series, Fourier series and so-forth can represent/approximate "any function". True but effectively irrelevant. What matters is what function a given methodology can be trained, programmed or whatever into representing.